Colon cancer is one of the leading causes of death in the U.S. The number of deaths can be largely reduced if polyps can be detected and treated at their early stage of development. Virtual colonoscopy is a new technology being developed to help doctors find polyps in three dimensional (3D) computed tomography (CT) image data. However, it currently requires that the colon be physically cleansed prior to the CT scan. This is very inconvenient and prevents virtual colonoscopy from being a general screening tool for a large population. In addition, years of training are required for a doctor to successfully identify polyps in the 3D CT image.
The size of a polyp is measured by its diameter. Usually, a polyp smaller than 6 mm is not of much clinical significance. Polyps bigger than 9 mm are very likely to be cancers and can be identified by doctors easily. It is most important for a polyp detection system to be able to detect polyps in the 6˜9 mm range since they may develop into cancers.
The task of automatic polyp detection is very challenging. First, the CT data is taken without bowel cleansing in order to minimize the inconvenience to patients. Tagged materials, such as stool, though mostly depicted as bright areas in the image, are a big distraction. Second, polyps of interest are very small and don't have unique intensity patterns, nor have any special shapes. It is hard to distinguish them from the colon wall, especially when they are surrounded by tagged material. Third, the volumetric data to be processed is massive (e.g., 400×512×512), which eliminates the possibility of using any computationally expensive methods. There is a need for a method for detecting polyps in three dimensional colon image data by using a generative model to capture the underlying generation process of the polyp.